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Rapid distributed task allocation for large-scale redundant agents: a confidence proxy-driven posterior correction and greedy swapping method

Published online by Cambridge University Press:  13 July 2026

Pei Chi ,
Chen Liu Open the ORCID record for Chen Liu [Opens in a new window] ,
Jiang Zhao ,
Xuyang Gao  and
Yingxun Wang
Pei Chi
Affiliation:
Beihang University Institute of Unmanned System, China
Chen Liu
Affiliation:
Beihang University School of Automation Science and Electrical Engineering , China
Jiang Zhao*
Affiliation:
Beihang University School of Automation Science and Electrical Engineering , China
Xuyang Gao
Affiliation:
Beihang University Institute of Unmanned System, China
Yingxun Wang
Affiliation:
Beihang University Institute of Unmanned System, China
*
Corresponding author: Jiang Zhao; Email: jzhao@buaa.edu.cn
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Abstract

For large-scale problems, efficiently optimizing task allocation with local communication and computing resources is challenging. Existing algorithms often fall short in task allocation computation time. Inspired by Bayesian estimation, we propose a parametric heuristic algorithm for redundant agents that can effectively perform dynamic task allocation in distributed architectures. Firstly, the method includes a confidence proxy-driven posterior correction algorithm (CPPCA), which can iteratively satisfy the constraint of the number of agents for each task based on local communication. We also prove that CPPCA can strictly converge after the number of neighbors is larger than a certain value. Then, we design a greedy swapping algorithm (GSA) to reduce the total cost while satisfying the constraints. It is also proved that the algorithm can converge in a finite number of steps under the condition that the communication graph is connected. Finally, the feasibility of the method is verified by simulation experiments. It shows that the confidence proxy-driven posterior correction and greedy swapping (CPPC-GS) method has significant advantages over the baseline algorithms in solving large-scale problems under the premise of redundant agents. It also has good results in the face of dynamic task allocation. Furthermore, the virtual-real fusion experiment bridges the “modeling gap” by validating the algorithm’s resilience to real-world engineering constraints, such as non-ideal communication and hardware heterogeneity, ensuring a seamless transition from theoretical frameworks to robust practical deployment.

Keywords

redundant agentsdynamic task allocationdistributed architectureconfidence proxygreedy swapping method

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Type
Research Article
Information
Robotica , First View , pp. 1 - 30
Copyright
© The Author(s), 2026. Published by Cambridge University Press

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References

Prorok, A., “Robust assignment using redundant robots on transport networks with uncertain travel time,” IEEE Trans Autom Sci Eng. 17(4), 2025–2037 (2020).10.1109/TASE.2020.2986641CrossRefGoogle Scholar
Abdelli, A., Yachir, A., Amamra, A. and Khaldi, B., “Maximum likelihood estimate sharing for collective perception in static environments for swarm robotics,” Robotica. 41(9), 2754–2773 (2023).10.1017/S0263574723000668CrossRefGoogle Scholar
Ramos, G. S., Pinto, M. F. and Haddad, D. B., “Advancing uav swarm autonomy with arcog-net for task allocation, path planning, and formation control,” Robotica. 43(7), 2565–2609 (2025).10.1017/S0263574725101914CrossRefGoogle Scholar
Seenu, N., Kuppan Chetty, R. M., Ramya, M. M. and Janardhanan, M. N., “Review on state-of-the-art dynamic task allocation strategies for multiple-robot systems,” Industrial Robot: Int J Robot Res Appl. 47(6), 929–942 (2020).Google Scholar
Raptis, I. and Sullivan, B., “Arachne system: An accessible miniaturized mobile multi-robot platform,” Robotica. 43(7), 2610–2643 (2025).10.1017/S0263574725101884CrossRefGoogle Scholar
Malencia, M., Kumar, V., Pappas, G. and Prorok, A., “Fair robust assignment using redundancy,” IEEE Robot Autom Lett. 6(2), 4217–4224 (2021).10.1109/LRA.2021.3067283CrossRefGoogle Scholar
Deng, Z. and Chen, T., “Distributed algorithm design for constrained resource allocation problems with high-order multi-agent systems,” Automatica. 144, 110492 (2022).10.1016/j.automatica.2022.110492CrossRefGoogle Scholar
Gerkey, B. P. and Matarić, M. J., “A formal analysis and taxonomy of task allocation in multi-robot systems,” The Int J Robot Res. 23(9), 939–954 (2004).10.1177/0278364904045564CrossRefGoogle Scholar
Choi, H.-L., Brunet, L. and How, J. P., “Consensus-based decentralized auctions for robust task allocation,” IEEE Trans Robot. 25(4), 912–926 (2009).10.1109/TRO.2009.2022423CrossRefGoogle Scholar
Ghassemi, P. and Chowdhury, S., “Multi-robot task allocation in disaster response: Addressing dynamic tasks with deadlines and robots with range and payload constraints,” Robot Auton Syst. 147, 103905 (2022).10.1016/j.robot.2021.103905CrossRefGoogle Scholar
Yan, F. and Di, K., “Solving the multi-robot task allocation with functional tasks based on a hyper-heuristic algorithm,” Appl Soft Comput. 146, 110628 (2023).10.1016/j.asoc.2023.110628CrossRefGoogle Scholar
Saeedvand, S., Aghdasi, H. S. and Baltes, J., “Robust multi-objective multi-humanoid robots task allocation based on novel hybrid metaheuristic algorithm,” Appl Intell. 49(12), 4097–4127 (2019).10.1007/s10489-019-01475-8CrossRefGoogle Scholar
Parker, L. E., “L-alliance: Task-oriented multi-robot learning in behavior-based systems,” Adv Robot. 11(4), 305–322 (1996).10.1163/156855397X00344CrossRefGoogle Scholar
Wu, W., Dong, J., Sun, Y. and Yu, F. R., “Heterogeneous Markov decision process model for joint resource allocation and task scheduling in network slicing enabled internet of vehicles,” IEEE Wirel Commun Lett. 11(6), 1118–1122 (2022).10.1109/LWC.2022.3152177CrossRefGoogle Scholar
Pérez-Escudero, A. and de Polavieja, G., “Collective animal behavior from Bayesian estimation and probability matching,” PLoS Comput Biol. 7, e1002282 (2011).10.1371/journal.pcbi.1002282CrossRefGoogle ScholarPubMed
Chen, Z., He, C., He, Z. and Chen, M., “Bd-adopt: A hybrid dcop algorithm with best-first and depth-first search strategies,” Artif Intell Rev. 50, 161–199 (2018).10.1007/s10462-017-9540-zCrossRefGoogle Scholar
Chen, D., Deng, Y., Chen, Z., Zhang, W. and He, Z.,“HS-CAI: A Hybrid DCOP Algorithm via Combining Search with Context-Based Inference,” Proceedings of the AAAI Conference on Artificial Intelligence (2020) 34, pp.7087–7094.Google Scholar
Nguyen, D. T., Yeoh, W., Lau, H. C. and Zivan, R., “Distributed gibbs: A linear-space sampling-based dcop algorithm,” J Artif Intell Res. 64, 705–748 (2019).10.1613/jair.1.11400CrossRefGoogle Scholar
Liu, X., Jing, T. and Hou, L., “An fw–ga hybrid algorithm combined with clustering for uav forest fire reconnaissance task assignment,” Mathematics. 11(10), 2400 (2023).10.3390/math11102400CrossRefGoogle Scholar
Liu, J., Wang, Q., Xu, Y. and Liu, C.. A Novel Distributed Method for Time-Critical Task Allocation Problems in Multi-UAV System ICC 2021-IEEE International Conference on Communications, IEEE (2021) pp. 1–6.Google Scholar
Zhou, Y., Lu, F., Xu, J. and Wu, L., “Multi-agent cross-domain collaborative task allocation problem based on multi-strategy improved dung beetle optimization algorithm,” Appl Sci. 14(16), 7175 (2024).10.3390/app14167175CrossRefGoogle Scholar
Martin, J. G., García, R. A. and Camacho, E. F., “Event-milp-based task allocation for heterogeneous robotic sensor network for thermosolar plants,” J Intell Robot Syst. 102(1), 1 (2021).10.1007/s10846-021-01346-wCrossRefGoogle Scholar
Chopra, S., Notarstefano, G., Rice, M. and Egerstedt, M., “A distributed version of the Hungarian method for multirobot assignment,” IEEE T Robot. 33(4), 932–947 (2017).10.1109/TRO.2017.2693377CrossRefGoogle Scholar
Notomista, G., Mayya, S., Emam, Y., Kroninger, C., Bohannon, A., Hutchinson, S. and Egerstedt, M., “A resilient and energy-aware task allocation framework for heterogeneous multirobot systems,” IEEE Trans Robot. 38(1), 159–179 (2021).10.1109/TRO.2021.3102379CrossRefGoogle Scholar
Govoni, L. and Cristofaro, A.. “Decentralized Task Allocation for Redundant Multi-Robot Systems: An Iterative Consensus Approach,” In: 2024 IEEE 18th International Conference on Control & Automation (ICCA), IEEE (2024) pp. 804–809.Google Scholar
Zhang, M., Chen, S., Wei, Z. and Wu, Y., “Preference-matched multitask assignment for group socialization under mobile crowdsensing,” Sensors. 23(4), 2275 (2023).10.3390/s23042275CrossRefGoogle Scholar
Grillo, A., Carpin, S., Recchiuto, C. T. and Sgorbissa, A., “Trust as a metric for auction-based task assignment in a cooperative team of robots with heterogeneous capabilities,” Robot Auton Syst. 157, 104266 (2022).10.1016/j.robot.2022.104266CrossRefGoogle Scholar
Oh, G., Kim, Y., Ahn, J. and Choi, H.-L., “Market-based task assignment for cooperative timing missions in dynamic environments,” J Intell Robot Syst. 87, 97–123 (2017).10.1007/s10846-017-0493-xCrossRefGoogle Scholar
Yin, Y., Guo, Y., Su, Q. and Wang, Z., “Task allocation of multiple unmanned aerial vehicles based on deep transfer reinforcement learning,” Drones. 6(8), 215 (2022).10.3390/drones6080215CrossRefGoogle Scholar
Wang, Z., Zhu, J., Guo, X. P., Ma, Y. T. and Li, Z., “Distributed task allocation method based on self-awareness of autonomous robots,” The J Supercomput. 76(2), 831–843 (2020).10.1007/s11227-019-02987-3CrossRefGoogle Scholar
Chen, Y., Rosolia, U. and Ames, A. D., “Decentralized task and path planning for multi-robot systems,” IEEE Robot Autom Lett. 6(3), 4337–4344 (2021).10.1109/LRA.2021.3068103CrossRefGoogle Scholar
Yan, S. and Xia, Y., “A distributed task allocation method for heterogeneous uavs in dynamic and communication-constrained environments,” The J Supercomput. 81(1), 11 (2025).10.1007/s11227-024-06517-8CrossRefGoogle Scholar
Wang, Z., Li, M., Li, J., Cao, J. and Wang, H., “A Task Allocation Algorithm Based on Market Mechanism for Multiple Robot Systems,” In: 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR), IEEE (2016) pp. 150–155.Google Scholar
Su, X., Wang, Y., Jia, X., Guo, L. and Ding, Z., “Two innovative coalition formation models for dynamic task allocation in disaster rescues,” J Syst Sci Syst Eng. 27(2), 215–230 (2018).10.1007/s11518-018-5365-9CrossRefGoogle Scholar
Liu, J., Tan, W., Liang, Z. and Ding, K., “Multi-Task Allocation Under Multiple Constraints in Mobile Crowdsensing,” In: International Conference on Human Centered Computing, Springer (2021) pp. 183–195.Google Scholar
Patel, R., Rudnick-Cohen, E., Azarm, S., Otte, M., Xu, H. and Herrmann, J. W., “Decentralized Task Allocation in Multi-Agent Systems Using a Decentralized Genetic Algorithm,” In: 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE (2020) pp. 3770–3776.Google Scholar
AMOV Lab, “Prometheus: Open source software for autonomous drones,” (2021). https://github.com/amov-lab/Prometheus.Google Scholar